![Research background
2 / 17
• End-to-end (E2E) Text-to-Speech (TTS)[Wang+17]
・High quality but low controllability
・Difficulty in correcting accent errors
(cause of miscommunication, especially in a tonal language e.g., Japanese)
High
Low
E2E TTS
model
a m e
a m e
E2E TTS
model
Text
a
me
a
me
Prosody modeling and control in E2E TTS are a very
important and challenging task.
High
Low](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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- 1. AVATAR SYMBIOTIC SOCIETY Adaptive End-to-End Text-to-Speech Synthesis Based on Error Correction Feedback from Humans Kazuki Fujii, Yuki Saito, Hiroshi Saruwatari The University of Tokyo, Japan APSIPA ASC 2022 ThPM1-3.5 7 - 10 Nov. 2022・Chiang Mai, Thailand 1 / 17
- 2. Research background 2 / 17 • End-to-end (E2E) Text-to-Speech (TTS)[Wang+17] ・High quality but low controllability ・Difficulty in correcting accent errors (cause of miscommunication, especially in a tonal language e.g., Japanese) High Low E2E TTS model a m e a m e E2E TTS model Text a me a me Prosody modeling and control in E2E TTS are a very important and challenging task. High Low
- 3. Improving controllability using linguistic features 3 / 17 • Examples of linguistic features ・Full-context labels [Okamoto+19], [Okamoto+20] ・Phonetic and prosodic labels [Kurihara+21] • High controllability but need for prerequisite expertise “Adaptability”, the ability to easily correct mistakes, is not taken into consideration. Text analysis E2E TTS Input text → → linguistic feature → → Synthesized speech
- 4. Outline of this talk 4 / 17 • Goal: Improve both “controllability” and “adaptability” of E2E TTS ・To make it easy for users to correct accent errors in synthetic speech • Proposed: ・E2E TTS with a prosody predictor (improve “controllability” ) ・Human-in-the-Loop (HITL) framework (improve “adaptability”) • Results: ・Our HITL framework successfully corrects accent errors. ・Our method successfully achieves the same quality of synthetic speech as the conventional method.
- 5. Overview of proposed TTS model 5 / 17 • Backbone TTS model: FastSpeech2 [Ren+21] with trainable prosody predictor
- 6. Backbone TTS model: FastSpeech2 6 / 17 • FastSpeech2 [Ren+21] ・Modules for generating a mel-spectrogram of speech from a phoneme sequence ・Stable learning and inference Variance Adaptor Duration/pitch/energy predictor
- 7. Proposed method 7 / 17 • Prosody predictor: DNN to estimate pitch changes per syllable (mora) • Input: Phoneme embedding + Word embedding from BERT [Devlin+19] • Output: One of three prosodic symbols ("[" / "]" : raising / lowering pitch, • Loss function: MSE between predicted symbols and ground-truth (text analysis results) "_" : keeping accent unchanged)
- 8. Overview of proposed HITL framework 8 / 17 • Motivation: Improves the adaptability of synthetic speech • Approach: Involves multiple listeners in the process of correcting accent errors … … (a) (me) a me a me
- 9. HITL accent error correction: Feedback aggregation 9 / 17 • Challenging point: Differences of listeners' error correction abilities ・Simple Way: Choose one from multiple accent annotations ・Proposed: Aggregate in the following ways (actual accent is Low(L) High(H) H H) ↑ Good Bad ↓ Random Selector Bad accent selected Annotated accent Estimated ability by MACE Listener 1 L L L L Low ability Listener 2 L L L L Low ability Listener 3 L H H H High ability Mode L L L L MACE [Hovy+13] L H H H Low quarty speech
- 10. Experimental evaluation 10 / 17 • Evaluation targets ・TTS model ・HITL framework • Objective evaluation criterion ・RMSE of the logF0 between synthetic and natural speech • Subjective evaluation criterion ・Mean Opinion Score (MOS) test ・Listeners rated the naturalness of each sample on a 5-point scale (1: very poor--5: very good). ・The number of listeners was 50, and each listened to 20 speech samples. ・Preference AB test ・Listeners evaluated 10 pairs of speech samples synthesized by a specific method-pair. ・The number of listeners for each AB test was 25.
- 11. Experimental setting for TTS model 11 / 17 • Experimental conditions for TTS model • Compared methods for TTS model TTS model FastSpeech2 [Ren+21] Train/eval. data of TTS model and Prosody predictor JSUT corpus [Takamichi+20], BASIC5000 subset, 4,488 / 512 sentences ・FS2 ・FS2+Symbol ・FS2+Predictor (target) ・FS2+Predictor …Proposed
- 12. • Objective evaluation of TTS model Result of objective evaluation 12 / 17 Bad Good worsen This means that the prediction error of the prosody predictor significantly degrades quality
- 13. • Objective evaluation of TTS model Result of objective evaluation 12 / 17 Bad Good comparable Suggests that natural speech can be synthesized if correct accent is obtained through feedback
- 14. • Subjective evaluation of TTS model ・Preference score for prosody naturalness of synthetic speech …BOLD denotes a significant difference between the two methods Compared method Preference score FS2 vs. FS2+Predictor 0.552 vs. 0.448 FS2 vs. FS2+Predictor (target) 0.268 vs. 0.732 FS2+Symbol vs. FS2+Predictor 0.768 vs. 0.232 FS2+Symbol vs. FS2+Predictor (target) 0.520 vs. 0.480 FS2 vs. FS2+Symbols 0.248 vs. 0.752 Result of subjective evaluation 13 / 17 better better
- 15. • Subjective evaluation of TTS model ・Preference score for prosody naturalness of synthetic speech …BOLD denotes a significant difference between the two methods Result of subjective evaluation 13 / 17 No significant difference Compared method Preference score FS2 vs. FS2+Predictor 0.552 vs. 0.448 FS2 vs. FS2+Predictor (target) 0.268 vs. 0.732 FS2+Symbol vs. FS2+Predictor 0.768 vs. 0.232 FS2+Symbol vs. FS2+Predictor (target) 0.520 vs. 0.480 FS2 vs. FS2+Symbols 0.248 vs. 0.752
- 16. • Subjective evaluation of TTS model ・Preference score for prosody naturalness of synthetic speech …BOLD denotes a significant difference between the two methods Result of subjective evaluation 13 / 17 No significant difference Compared method Preference score FS2 vs. FS2+Predictor 0.552 vs. 0.448 FS2 vs. FS2+Predictor (target) 0.268 vs. 0.732 FS2+Symbol vs. FS2+Predictor 0.768 vs. 0.232 FS2+Symbol vs. FS2+Predictor (target) 0.520 vs. 0.480 FS2 vs. FS2+Symbols 0.248 vs. 0.752 These results suggests that our method improves the prosodic naturalness of synthetic speech
- 17. Experimental setting for HITL framework 14 / 17 • Experimental conditions for HITL framework • Compared methods for HITL framework ・Mode, MACE, Best, Median, Worst Listerner hiring platform Lancers (crowdsourcing platform) Dataset, Participant Japanese female speaker (JSUT corpus) Unused data for training TTS models and Prosody Predictor) 100 centences, 15 person / sentences (=1,500 crowdworkers) HITL framework interface Initialize radio buttons with text analysis-derived accent information … 15 crowdworkers L H H H L L .... → H H H H L L .... → L H H H H L .... → E2E TTS … … logF0 RMSE between Ground-Truth Sort … … Good ↑ ↓ Bad ←Best ←Median ←Worst [input accents]
- 18. • Objective evaluation of HITL framework Result of objective evaluation 15 / 17 Bad Good →Methods w/ HITL worsen
- 19. • Objective evaluation of HITL framework Result of objective evaluation 15 / 17 Bad Good →Methods w/ HITL
- 20. • Objective evaluation of HITL framework Result of objective evaluation 15 / 17 Bad Good →Methods w/ HITL comparable These results suggests that error correction feedback can improve TTS quality
- 21. • Subjective evaluation of HITL framework ・Preference score for prosody naturalness of synthetic speech …BOLD are higher than those of FS2+Predictor Result of subjective evaluation 16 / 17 Method MOS 95% confidence interval FS2+Predictor 2.87 0.163 FS2+Predictor (target) 3.54 0.156 Best 3.62 0.151 Median 3.38 0.156 Worst 2.84 0.174 Mode 3.63 0.159 MACE 3.49 0.161 obviously lower
- 22. • Subjective evaluation of HITL framework ・Preference score for prosody naturalness of synthetic speech …BOLD are higher than those of FS2+Predictor Result of subjective evaluation 16 / 17 Method MOS 95% confidence interval FS2+Predictor 2.87 0.163 FS2+Predictor (target) 3.54 0.156 Best 3.62 0.151 Median 3.38 0.156 Worst 2.84 0.174 Mode 3.63 0.159 MACE 3.49 0.161 These results suggest that the prediction error of the prosody predictor degrades the naturalness of speech. obviously lower
- 23. Method MOS 95% confidence interval FS2+Predictor 2.87 0.163 FS2+Predictor (target) 3.54 0.156 Best 3.62 0.151 Median 3.38 0.156 Worst 2.84 0.174 Mode 3.63 0.159 MACE 3.49 0.161 • Subjective evaluation of HITL framework ・Preference score for prosody naturalness of synthetic speech …BOLD are higher than those of FS2+Predictor Result of subjective evaluation 16 / 17 significantly higher
- 24. Method MOS 95% confidence interval FS2+Predictor 2.87 0.163 FS2+Predictor (target) 3.54 0.156 Best 3.62 0.151 Median 3.38 0.156 Worst 2.84 0.174 Mode 3.63 0.159 MACE 3.49 0.161 • Subjective evaluation of HITL framework ・Preference score for prosody naturalness of synthetic speech …BOLD are higher than those of FS2+Predictor Result of subjective evaluation 16 / 17 significantly higher These results suggests that our method improves the prosodic naturalness of synthetic speech
- 25. Summary • Goal: Improve both “controllability” and “adaptability” of E2E TTS ・To make it easy for users to correct accent errors in synthetic speech • Proposed: ・E2E TTS with a prosody predictor (improve “controllability”) ・Human-in-the-Loop (HITL) framework (improve “adaptability”) • Results: ・Our HITL framework successfully corrected accent errors. ・Our method successfully achieved the same quality of synthetic speech as the conventional method. • Future work ・User interface of feedback ・How to integrate the obtained prosodic sequences. 17 / 17 Thank you for your attention!